Invited Perspective: Screening in Academe: A Perspective on Implementation of University-Based Small Molecule Screening
Bibliographic record
Abstract
With the emergence ofsmall molecule screening as a power ful research tool in biology, biomolecular screening has arrived in the laboratories ofacademe. More and more, researchers in universities and hospital research institutes are recognizing the power ofsmall molecules as probes ofbiochemical and biological systems. At McMaster University in Hamilton, Canada, we have established a state-of-the-art small molecule screening laboratory that became fully operational a little more than a year ago (http:// hts.mcmaster.ca). It is with considerable enthusiasm, as the founding director of that laboratory, that I offer the following perspectives on screening in an academic setting and on my own experiences in setting up such a facility. Since the establishment ofthe first academic screening operations, including, for example, that of the Harvard Institute of Chemistry and Cell Biology in the late 1990s, there has been a groundswell ofinterest among academic researchers in highthroughput screening (HTS). Today, biological researchers in most research-intensive academic institutions are, at the very least, considering the establishment ofcapabilities and a presence in small molecule screening to fuel research activities in the emerging field ofchemical biology. Nevertheless, the implementation ofa reason ably capable screening facility with its associated liquid-handling, instrumentation, compound, and information management systems is a complex, costly, and energetic undertaking. In setting up a state-of-the-art screening laboratory, it has been instructive for me to reflect on how advances in HTS in the private sector have exerted recent influence on research directions in bio logical research at universities and research institutes. This trend contrasts with the conventional academic view ofinnovation that has emerging technology moving from university to industry. Advances in biological research have, ofcourse, been among the most celebrated university-based innovations. The once purely academic pursuits ofbiological chemistry and cell biology have slowly but firmly established themselves in the research paradigm off ormerly chemistry-centered pharma, beginning with revolu tionary advances in molecular biology in the late 1970s. It is per haps ironic, ifnot remarkable, then, that similarly revolutionary progress made by high-throughput screeners in the pharmaceutical sector has affected the technology now available to chemical biologists in academe. Equipped with robust robotics, information systems, and wellestablished screening methodologies, all developed by and large in the biotechnology and pharmaceutical sector, biochemists and cell biologists in academe are turning in earnest to small molecule screening as a fresh approach to discovering molecular probes of systems under their study. With the freedom to innovate and publish, academic screeners will now surely be in a position to return the favor to their private-sector colleagues with new discoveries and new approaches. It is clearly fitting that the Society for Biomolecular Screening (SBS), founded by private-sector researchers, has taken steps to welcome screeners from academe with the formation of the Academic Outreach Committee in the spring of2003. The committee is composed ofa mix ofindividuals from industry and academe, including myself, who have a keen interest in building bridges among academic screeners and their colleagues in pharmaceutical, biotechnology, and instrumentation companies. The committee also has the mandate offurthering the interests and profile of academic screeners in the SBS and has the potential to function as a nucleus for the exchange of ideas and experiences among academic screeners. Our own HTS lab was founded by 3 principal investigators at McMaster whose research interests include antimicrobial research (Gerry Wright and I) and materials science (John Brennan). The genesis and funding for the laboratory came as part of a provincewide initiative in genomics and included close partnerships with combinatorial chemistry laboratories at York University and the University ofToronto. The thinking was that the chemists and screeners could work together, for example, in lead optimization activities. Indeed, the practicality ofattracting the interest of collaborators in synthetic chemistry for downstream optimization of hits from commercial libraries is frequently a concern among
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.053 | 0.028 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".